In this article, we consider numerical methods for fully nonlinear PDEs
based on machine learning techniques that are conducted in two steps.
The starting point of our approach is to rewrite the PDE (REF ) with convex Hamiltonian in its stochastic control representation form following the duality arguments of [1]}. An approximation of the associated optimal feedback control is then obtained using a neural network by a global optimization, as described in [2]} and [3]}.
Based on this control approximation, two main approaches using neural networks are then developed in order to approximate the value function, hence the solution of the initial PDE on the space-time domain, which is formulated as a conditional expectation with respect to the approximate optimal state process.
